April 17, 2017
Computational. Network. Epidemiology.
(Computational) (Infectious) Disease Modeling
Two main types of models:
SIR Models
\[ \begin{align*} \frac{dS}{dt} &= -b S(t) I(t)\\ \frac{dI}{dt} &= b S(t) I(t) - kI(t)\\ \frac{dR}{dt} &= k I(t) \end{align*} \]
Pros
Cons
The agents repeat these rules (ticks/cycles)
Observe complex system dynamics from the bottom-up through emergence
Segregation
Wolf Sheep Predation
Virus on a Network
Pros
Cons
Watts 2002: A simple model of global cascades on random networks
Diffution of Innovations:
Early adopters ~ vulnerable nodes
More early adopters, higher chance of innovation, but they need to be connected (structure)
\(\phi\) and \(k\) are 2 parameters we can change
https://github.com/chendaniely/gbcb_seminar_presentation_1/raw/master/figures/p_flipped_all.png
The Watts model can be used to model any binary outcome
From a public health and epidemiology perspective, this outcome can be a particular behavior or action.
However, our decision making process is not that simple.
TRA Health behavior model
beliefs \(\rightarrow\) (attitudes & social context) \(\rightarrow\) intention \(\rightarrow\) behavior
Questions: - How to summarize the NN Simulation data - Signal Decomposition/analysis - Projections (t-sne)
Main Lesson: Running software for non-technical people (shiny?)
Main Findings: "The sectors in the US mass fatality infrastructure report suboptimal capability to respond. National leadership is needed…"
Lessons Learned: Data quality governs analysis
Barbara Sheehan
Looked for patients who had no re-admissions, re-admissions, and multiple re-admissions
Lessons Learned: EHR Data is really messy
Open [Access, Source, Education, Data]
https://github.com/chendaniely/computational-project-cookie-cutter
Computational. Network. Epidemiology.